


import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

__UpperCamelCase     		=							[
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

__UpperCamelCase     		=							[
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

__UpperCamelCase     		=							[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
						# loop over downblocks/upblocks

						for j in range(2):
												# loop over resnets/attentions for downblocks
												__UpperCamelCase     		=							f'''down_blocks.{i}.resnets.{j}.'''
												__UpperCamelCase     		=							f'''input_blocks.{3*i + j + 1}.0.'''
												unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

												if i < 3:
																		# no attention layers in down_blocks.3
																		__UpperCamelCase     		=							f'''down_blocks.{i}.attentions.{j}.'''
																		__UpperCamelCase     		=							f'''input_blocks.{3*i + j + 1}.1.'''
																		unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

						for j in range(3):
												# loop over resnets/attentions for upblocks
												__UpperCamelCase     		=							f'''up_blocks.{i}.resnets.{j}.'''
												__UpperCamelCase     		=							f'''output_blocks.{3*i + j}.0.'''
												unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

												if i > 0:
																		# no attention layers in up_blocks.0
																		__UpperCamelCase     		=							f'''up_blocks.{i}.attentions.{j}.'''
																		__UpperCamelCase     		=							f'''output_blocks.{3*i + j}.1.'''
																		unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

						if i < 3:
												# no downsample in down_blocks.3
												__UpperCamelCase     		=							f'''down_blocks.{i}.downsamplers.0.conv.'''
												__UpperCamelCase     		=							f'''input_blocks.{3*(i+1)}.0.op.'''
												unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

												# no upsample in up_blocks.3
												__UpperCamelCase     		=							f'''up_blocks.{i}.upsamplers.0.'''
												__UpperCamelCase     		=							f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
												unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__UpperCamelCase     		=							'mid_block.attentions.0.'
__UpperCamelCase     		=							'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
						__UpperCamelCase     		=							f'''mid_block.resnets.{j}.'''
						__UpperCamelCase     		=							f'''middle_block.{2*j}.'''
						unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def 							UpperCamelCase_(					_A :Optional[int]	)->		str:
			# buyer beware: this is a *brittle* function,
			# and correct output requires that all of these pieces interact in
			# the exact order in which I have arranged them.
			UpperCamelCase__       =							{k: k for k in unet_state_dict.keys()}
			for sd_name, hf_name in unet_conversion_map:
						UpperCamelCase__       =							sd_name
			for k, v in mapping.items():
						if "resnets" in k:
									for sd_part, hf_part in unet_conversion_map_resnet:
												UpperCamelCase__       =							v.replace(_A					,  _A	)
									UpperCamelCase__       =							v
			for k, v in mapping.items():
						for sd_part, hf_part in unet_conversion_map_layer:
									UpperCamelCase__       =							v.replace(_A					,  _A	)
						UpperCamelCase__       =							v
			UpperCamelCase__       =							{v: unet_state_dict[k] for k, v in mapping.items()}
			return new_state_dict


# ================#
# VAE Conversion #
# ================#

__UpperCamelCase     		=							[
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
						# down_blocks have two resnets
						for j in range(2):
												__UpperCamelCase     		=							f'''encoder.down_blocks.{i}.resnets.{j}.'''
												__UpperCamelCase     		=							f'''encoder.down.{i}.block.{j}.'''
												vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

						if i < 3:
												__UpperCamelCase     		=							f'''down_blocks.{i}.downsamplers.0.'''
												__UpperCamelCase     		=							f'''down.{i}.downsample.'''
												vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

												__UpperCamelCase     		=							f'''up_blocks.{i}.upsamplers.0.'''
												__UpperCamelCase     		=							f'''up.{3-i}.upsample.'''
												vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

						# up_blocks have three resnets
						# also, up blocks in hf are numbered in reverse from sd
						for j in range(3):
												__UpperCamelCase     		=							f'''decoder.up_blocks.{i}.resnets.{j}.'''
												__UpperCamelCase     		=							f'''decoder.up.{3-i}.block.{j}.'''
												vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
						__UpperCamelCase     		=							f'''mid_block.resnets.{i}.'''
						__UpperCamelCase     		=							f'''mid.block_{i+1}.'''
						vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


__UpperCamelCase     		=							[
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]
def 							UpperCamelCase_(					_A :Optional[Any]	)->		Tuple:
			# convert HF linear weights to SD conv2d weights
			return w.reshape(*w.shape					,  1					,  1	)
def 							UpperCamelCase_(					_A :Dict	)->		Dict:
			UpperCamelCase__       =							{k: k for k in vae_state_dict.keys()}
			for k, v in mapping.items():
						for sd_part, hf_part in vae_conversion_map:
									UpperCamelCase__       =							v.replace(_A					,  _A	)
						UpperCamelCase__       =							v
			for k, v in mapping.items():
						if "attentions" in k:
									for sd_part, hf_part in vae_conversion_map_attn:
												UpperCamelCase__       =							v.replace(_A					,  _A	)
									UpperCamelCase__       =							v
			UpperCamelCase__       =							{v: vae_state_dict[k] for k, v in mapping.items()}
			UpperCamelCase__       =							["q", "k", "v", "proj_out"]
			for k, v in new_state_dict.items():
						for weight_name in weights_to_convert:
									if F'''mid.attn_1.{weight_name}.weight''' in k:
												print(F'''Reshaping {k} for SD format'''	)
												UpperCamelCase__       =							reshape_weight_for_sd(_A	)
			return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


__UpperCamelCase     		=							[
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
__UpperCamelCase     		=							{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__UpperCamelCase     		=							re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__UpperCamelCase     		=							{'q': 0, 'k': 1, 'v': 2}
def 							UpperCamelCase_(					_A :str	)->		List[str]:
			UpperCamelCase__       =							{}
			UpperCamelCase__       =							{}
			UpperCamelCase__       =							{}
			for k, v in text_enc_dict.items():
						if (
						    k.endswith(".self_attn.q_proj.weight"	)
						    or k.endswith(".self_attn.k_proj.weight"	)
						    or k.endswith(".self_attn.v_proj.weight"	)
						):
									UpperCamelCase__       =							k[: -len(".q_proj.weight"	)]
									UpperCamelCase__       =							k[-len("q_proj.weight"	)]
									if k_pre not in capture_qkv_weight:
												UpperCamelCase__       =							[None, None, None]
									UpperCamelCase__       =							v
									continue

						if (
						    k.endswith(".self_attn.q_proj.bias"	)
						    or k.endswith(".self_attn.k_proj.bias"	)
						    or k.endswith(".self_attn.v_proj.bias"	)
						):
									UpperCamelCase__       =							k[: -len(".q_proj.bias"	)]
									UpperCamelCase__       =							k[-len("q_proj.bias"	)]
									if k_pre not in capture_qkv_bias:
												UpperCamelCase__       =							[None, None, None]
									UpperCamelCase__       =							v
									continue

						UpperCamelCase__       =							textenc_pattern.sub(lambda _A	: protected[re.escape(m.group(0	)	)]					,  _A	)
						UpperCamelCase__       =							v

			for k_pre, tensors in capture_qkv_weight.items():
						if None in tensors:
									raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"	)
						UpperCamelCase__       =							textenc_pattern.sub(lambda _A	: protected[re.escape(m.group(0	)	)]					,  _A	)
						UpperCamelCase__       =							torch.cat(_A	)

			for k_pre, tensors in capture_qkv_bias.items():
						if None in tensors:
									raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"	)
						UpperCamelCase__       =							textenc_pattern.sub(lambda _A	: protected[re.escape(m.group(0	)	)]					,  _A	)
						UpperCamelCase__       =							torch.cat(_A	)

			return new_state_dict
def 							UpperCamelCase_(					_A :Union[str, Any]	)->		Dict:
			return text_enc_dict


if __name__ == "__main__":
						__UpperCamelCase     		=							argparse.ArgumentParser()

						parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
						parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
						parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
						parser.add_argument(
						    '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
						)

						__UpperCamelCase     		=							parser.parse_args()

						assert args.model_path is not None, "Must provide a model path!"

						assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

						# Path for safetensors
						__UpperCamelCase     		=							osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
						__UpperCamelCase     		=							osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
						__UpperCamelCase     		=							osp.join(args.model_path, 'text_encoder', 'model.safetensors')

						# Load models from safetensors if it exists, if it doesn't pytorch
						if osp.exists(unet_path):
												__UpperCamelCase     		=							load_file(unet_path, device='cpu')
						else:
												__UpperCamelCase     		=							osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
												__UpperCamelCase     		=							torch.load(unet_path, map_location='cpu')

						if osp.exists(vae_path):
												__UpperCamelCase     		=							load_file(vae_path, device='cpu')
						else:
												__UpperCamelCase     		=							osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
												__UpperCamelCase     		=							torch.load(vae_path, map_location='cpu')

						if osp.exists(text_enc_path):
												__UpperCamelCase     		=							load_file(text_enc_path, device='cpu')
						else:
												__UpperCamelCase     		=							osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
												__UpperCamelCase     		=							torch.load(text_enc_path, map_location='cpu')

						# Convert the UNet model
						__UpperCamelCase     		=							convert_unet_state_dict(unet_state_dict)
						__UpperCamelCase     		=							{'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

						# Convert the VAE model
						__UpperCamelCase     		=							convert_vae_state_dict(vae_state_dict)
						__UpperCamelCase     		=							{'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

						# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
						__UpperCamelCase     		=							'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

						if is_vaa_model:
												# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
												__UpperCamelCase     		=							{'transformer.' + k: v for k, v in text_enc_dict.items()}
												__UpperCamelCase     		=							convert_text_enc_state_dict_vaa(text_enc_dict)
												__UpperCamelCase     		=							{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
						else:
												__UpperCamelCase     		=							convert_text_enc_state_dict(text_enc_dict)
												__UpperCamelCase     		=							{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

						# Put together new checkpoint
						__UpperCamelCase     		=							{**unet_state_dict, **vae_state_dict, **text_enc_dict}
						if args.half:
												__UpperCamelCase     		=							{k: v.half() for k, v in state_dict.items()}

						if args.use_safetensors:
												save_file(state_dict, args.checkpoint_path)
						else:
												__UpperCamelCase     		=							{'state_dict': state_dict}
												torch.save(state_dict, args.checkpoint_path)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase     		=							{
    'configuration_xlm_roberta_xl': [
        'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaXLConfig',
        'XLMRobertaXLOnnxConfig',
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase     		=							[
						    'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'XLMRobertaXLForCausalLM',
						    'XLMRobertaXLForMaskedLM',
						    'XLMRobertaXLForMultipleChoice',
						    'XLMRobertaXLForQuestionAnswering',
						    'XLMRobertaXLForSequenceClassification',
						    'XLMRobertaXLForTokenClassification',
						    'XLMRobertaXLModel',
						    'XLMRobertaXLPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    XLMRobertaXLConfig,
						    XLMRobertaXLOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_roberta_xl import (
												    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMRobertaXLForCausalLM,
												    XLMRobertaXLForMaskedLM,
												    XLMRobertaXLForMultipleChoice,
												    XLMRobertaXLForQuestionAnswering,
												    XLMRobertaXLForSequenceClassification,
												    XLMRobertaXLForTokenClassification,
												    XLMRobertaXLModel,
												    XLMRobertaXLPreTrainedModel,
												)

else:
						import sys

						__UpperCamelCase     		=							_LazyModule(__name__, globals()['__file__'], _import_structure)


